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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationThu, 22 Dec 2011 14:10:47 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245811116f9393giqg0ri1v.htm/, Retrieved Thu, 02 May 2024 23:09:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159873, Retrieved Thu, 02 May 2024 23:09:02 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [Classical Decompo...] [2011-11-30 17:01:51] [0b0939f48f9203aad97203a2adcb743b]
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Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159873&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159873&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159873&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1235.1235.1000
2280.7277.763374983192.319733517519982.936625016809760.909065055017082
3264.6264.0553748346012.155155579821760.544625165398704-0.564652462682712
4240.7240.1668591043582.054445476301620.533140895641532-0.919372979780313
5201.4201.8337876822581.88116254008675-0.433787682258163-1.42550634513946
6240.8235.4213001730792.022549944625725.378699826920811.11907933058613
7241.1239.7027663296932.032593971757911.397233670306960.079728309552817
8223.8223.2662849288291.950905732346470.533715071170602-0.651859538846223
9206.1205.2410905416691.862951090533410.85890945833095-0.705043251189397
10174.7174.8486192157581.72155192048466-0.148619215757786-1.13842391295259
11203.3199.0122812015321.819503104299854.287718798468470.792065735284206
12220.5217.9156803139661.893743719646492.584319686034270.602949240581813
13299.5287.119967185514-0.86628651894502812.38003281448612.81835605759697
14347.4342.0910355835220.3450416335334595.30896441647841.71406623303384
15338.3340.8055848337060.330679669543845-2.5055848337063-0.0570960657932064
16327.7327.3847713655650.2965171920688560.31522863443524-0.485075003887567
17351.6352.1986597865890.347969527091625-0.5986597865886050.864537394019871
18396.6389.7070908702850.4357304468717846.892909129714761.31027909171082
19438.8434.1951832084620.5426545940137734.604816791537631.55328232940504
20395.6401.5664787848490.462385326091128-5.96647878484908-1.16961955991549
21363.5364.855665844710.372703749442631-1.35566584470961-1.31072264508491
22378.8378.1295709612860.4046651387553640.6704290387138910.454924288636024
23357356.9273760614240.3472812532242180.0726239385755184-0.762102209796267
24369370.2772196565720.351864718274208-1.277219656571960.458058859970499
25464.8447.403222131374-0.92769620659495117.39677786862622.90606483608869
26479.1472.858100619748-0.6105365871338996.241899380251580.869432018576513
27431.3439.185769942804-0.875424103715443-7.88576994280393-1.1531225881721
28366.5377.229860064236-1.04053988260808-10.7298600642361-2.15414684255234
29326.3335.250583820214-1.10629603993556-8.95058382021411-1.44336253903777
30355.1350.895038431418-1.076916778899324.204961568581560.590497729354717
31331.6329.077140524318-1.116000838832432.52285947568242-0.731138065390755
32261.3272.30519024967-1.22218744284703-11.0051902496704-1.96191985127129
33249253.820401517309-1.25560323926166-4.82040151730855-0.608539854017583
34205.5208.749542376002-1.34652182175515-3.24954237600225-1.54483896728508
35235.6231.55129197533-1.299179185293544.048708024670040.851482368122667
36240.9248.602669675485-1.31909860543149-7.702669675485360.646708041555449
37264.9249.752837484753-1.3413374757398315.14716251524680.0902649582684895
38253.8244.154386286834-1.373512689582069.64561371316629-0.144411626151731
39232.3234.456388296084-1.43259688180749-2.1563882960838-0.289866009222027
40193.8204.250765094434-1.51987910587685-10.4507650944337-1.0143146242059
41177190.399575200062-1.53893995069403-13.3995752000624-0.434761230360682
42213.2204.64073378895-1.515653621239188.559266211049640.556244500728392
43207.2199.613085143013-1.521324185060377.58691485698661-0.123788328013792
44180.6191.099878975541-1.53313243494333-10.4998789755408-0.246444132233647
45188.6187.563140344584-1.536683654572931.03685965541644-0.0706273794046623
46175.4182.165907764184-1.54396395595142-6.7659077641838-0.136114307920771
47199195.084217657983-1.523860349316843.915782342017410.509765233392411
48179.6188.903317187931-1.51659122532706-9.30331718793088-0.164245010054623
49225.8205.989100997281-1.6161039473888619.81089900271910.669002887638946
50234221.235381158511-1.52871912688812.76461884148870.580156548370334
51200.2202.056803867243-1.63807052725028-1.85680386724347-0.61454564577158
52183.6193.312453744331-1.66109692683555-9.71245374433064-0.250356274749863
53178.2193.39329071464-1.65825526529454-15.19329071464020.0614159072831315
54203.2193.955528846781-1.655261329347399.244471153218940.0782711117938521
55208.5199.03164260544-1.645505058758149.468357394560460.237253734092753
56191.8201.554791213202-1.63896689692395-9.754791213202040.146928138622828
57172.8175.476764505281-1.68024206937595-2.6767645052809-0.861486482531513
58148158.458749768417-1.70642204085156-10.4587497684171-0.540752472236454
59159.4154.326471217919-1.708702609832275.07352878208063-0.0854812670148723
60154.5164.013375775531-1.72691031848988-9.513375775530660.402089568679853

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 235.1 & 235.1 & 0 & 0 & 0 \tabularnewline
2 & 280.7 & 277.76337498319 & 2.31973351751998 & 2.93662501680976 & 0.909065055017082 \tabularnewline
3 & 264.6 & 264.055374834601 & 2.15515557982176 & 0.544625165398704 & -0.564652462682712 \tabularnewline
4 & 240.7 & 240.166859104358 & 2.05444547630162 & 0.533140895641532 & -0.919372979780313 \tabularnewline
5 & 201.4 & 201.833787682258 & 1.88116254008675 & -0.433787682258163 & -1.42550634513946 \tabularnewline
6 & 240.8 & 235.421300173079 & 2.02254994462572 & 5.37869982692081 & 1.11907933058613 \tabularnewline
7 & 241.1 & 239.702766329693 & 2.03259397175791 & 1.39723367030696 & 0.079728309552817 \tabularnewline
8 & 223.8 & 223.266284928829 & 1.95090573234647 & 0.533715071170602 & -0.651859538846223 \tabularnewline
9 & 206.1 & 205.241090541669 & 1.86295109053341 & 0.85890945833095 & -0.705043251189397 \tabularnewline
10 & 174.7 & 174.848619215758 & 1.72155192048466 & -0.148619215757786 & -1.13842391295259 \tabularnewline
11 & 203.3 & 199.012281201532 & 1.81950310429985 & 4.28771879846847 & 0.792065735284206 \tabularnewline
12 & 220.5 & 217.915680313966 & 1.89374371964649 & 2.58431968603427 & 0.602949240581813 \tabularnewline
13 & 299.5 & 287.119967185514 & -0.866286518945028 & 12.3800328144861 & 2.81835605759697 \tabularnewline
14 & 347.4 & 342.091035583522 & 0.345041633533459 & 5.3089644164784 & 1.71406623303384 \tabularnewline
15 & 338.3 & 340.805584833706 & 0.330679669543845 & -2.5055848337063 & -0.0570960657932064 \tabularnewline
16 & 327.7 & 327.384771365565 & 0.296517192068856 & 0.31522863443524 & -0.485075003887567 \tabularnewline
17 & 351.6 & 352.198659786589 & 0.347969527091625 & -0.598659786588605 & 0.864537394019871 \tabularnewline
18 & 396.6 & 389.707090870285 & 0.435730446871784 & 6.89290912971476 & 1.31027909171082 \tabularnewline
19 & 438.8 & 434.195183208462 & 0.542654594013773 & 4.60481679153763 & 1.55328232940504 \tabularnewline
20 & 395.6 & 401.566478784849 & 0.462385326091128 & -5.96647878484908 & -1.16961955991549 \tabularnewline
21 & 363.5 & 364.85566584471 & 0.372703749442631 & -1.35566584470961 & -1.31072264508491 \tabularnewline
22 & 378.8 & 378.129570961286 & 0.404665138755364 & 0.670429038713891 & 0.454924288636024 \tabularnewline
23 & 357 & 356.927376061424 & 0.347281253224218 & 0.0726239385755184 & -0.762102209796267 \tabularnewline
24 & 369 & 370.277219656572 & 0.351864718274208 & -1.27721965657196 & 0.458058859970499 \tabularnewline
25 & 464.8 & 447.403222131374 & -0.927696206594951 & 17.3967778686262 & 2.90606483608869 \tabularnewline
26 & 479.1 & 472.858100619748 & -0.610536587133899 & 6.24189938025158 & 0.869432018576513 \tabularnewline
27 & 431.3 & 439.185769942804 & -0.875424103715443 & -7.88576994280393 & -1.1531225881721 \tabularnewline
28 & 366.5 & 377.229860064236 & -1.04053988260808 & -10.7298600642361 & -2.15414684255234 \tabularnewline
29 & 326.3 & 335.250583820214 & -1.10629603993556 & -8.95058382021411 & -1.44336253903777 \tabularnewline
30 & 355.1 & 350.895038431418 & -1.07691677889932 & 4.20496156858156 & 0.590497729354717 \tabularnewline
31 & 331.6 & 329.077140524318 & -1.11600083883243 & 2.52285947568242 & -0.731138065390755 \tabularnewline
32 & 261.3 & 272.30519024967 & -1.22218744284703 & -11.0051902496704 & -1.96191985127129 \tabularnewline
33 & 249 & 253.820401517309 & -1.25560323926166 & -4.82040151730855 & -0.608539854017583 \tabularnewline
34 & 205.5 & 208.749542376002 & -1.34652182175515 & -3.24954237600225 & -1.54483896728508 \tabularnewline
35 & 235.6 & 231.55129197533 & -1.29917918529354 & 4.04870802467004 & 0.851482368122667 \tabularnewline
36 & 240.9 & 248.602669675485 & -1.31909860543149 & -7.70266967548536 & 0.646708041555449 \tabularnewline
37 & 264.9 & 249.752837484753 & -1.34133747573983 & 15.1471625152468 & 0.0902649582684895 \tabularnewline
38 & 253.8 & 244.154386286834 & -1.37351268958206 & 9.64561371316629 & -0.144411626151731 \tabularnewline
39 & 232.3 & 234.456388296084 & -1.43259688180749 & -2.1563882960838 & -0.289866009222027 \tabularnewline
40 & 193.8 & 204.250765094434 & -1.51987910587685 & -10.4507650944337 & -1.0143146242059 \tabularnewline
41 & 177 & 190.399575200062 & -1.53893995069403 & -13.3995752000624 & -0.434761230360682 \tabularnewline
42 & 213.2 & 204.64073378895 & -1.51565362123918 & 8.55926621104964 & 0.556244500728392 \tabularnewline
43 & 207.2 & 199.613085143013 & -1.52132418506037 & 7.58691485698661 & -0.123788328013792 \tabularnewline
44 & 180.6 & 191.099878975541 & -1.53313243494333 & -10.4998789755408 & -0.246444132233647 \tabularnewline
45 & 188.6 & 187.563140344584 & -1.53668365457293 & 1.03685965541644 & -0.0706273794046623 \tabularnewline
46 & 175.4 & 182.165907764184 & -1.54396395595142 & -6.7659077641838 & -0.136114307920771 \tabularnewline
47 & 199 & 195.084217657983 & -1.52386034931684 & 3.91578234201741 & 0.509765233392411 \tabularnewline
48 & 179.6 & 188.903317187931 & -1.51659122532706 & -9.30331718793088 & -0.164245010054623 \tabularnewline
49 & 225.8 & 205.989100997281 & -1.61610394738886 & 19.8108990027191 & 0.669002887638946 \tabularnewline
50 & 234 & 221.235381158511 & -1.528719126888 & 12.7646188414887 & 0.580156548370334 \tabularnewline
51 & 200.2 & 202.056803867243 & -1.63807052725028 & -1.85680386724347 & -0.61454564577158 \tabularnewline
52 & 183.6 & 193.312453744331 & -1.66109692683555 & -9.71245374433064 & -0.250356274749863 \tabularnewline
53 & 178.2 & 193.39329071464 & -1.65825526529454 & -15.1932907146402 & 0.0614159072831315 \tabularnewline
54 & 203.2 & 193.955528846781 & -1.65526132934739 & 9.24447115321894 & 0.0782711117938521 \tabularnewline
55 & 208.5 & 199.03164260544 & -1.64550505875814 & 9.46835739456046 & 0.237253734092753 \tabularnewline
56 & 191.8 & 201.554791213202 & -1.63896689692395 & -9.75479121320204 & 0.146928138622828 \tabularnewline
57 & 172.8 & 175.476764505281 & -1.68024206937595 & -2.6767645052809 & -0.861486482531513 \tabularnewline
58 & 148 & 158.458749768417 & -1.70642204085156 & -10.4587497684171 & -0.540752472236454 \tabularnewline
59 & 159.4 & 154.326471217919 & -1.70870260983227 & 5.07352878208063 & -0.0854812670148723 \tabularnewline
60 & 154.5 & 164.013375775531 & -1.72691031848988 & -9.51337577553066 & 0.402089568679853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159873&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]235.1[/C][C]235.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]280.7[/C][C]277.76337498319[/C][C]2.31973351751998[/C][C]2.93662501680976[/C][C]0.909065055017082[/C][/ROW]
[ROW][C]3[/C][C]264.6[/C][C]264.055374834601[/C][C]2.15515557982176[/C][C]0.544625165398704[/C][C]-0.564652462682712[/C][/ROW]
[ROW][C]4[/C][C]240.7[/C][C]240.166859104358[/C][C]2.05444547630162[/C][C]0.533140895641532[/C][C]-0.919372979780313[/C][/ROW]
[ROW][C]5[/C][C]201.4[/C][C]201.833787682258[/C][C]1.88116254008675[/C][C]-0.433787682258163[/C][C]-1.42550634513946[/C][/ROW]
[ROW][C]6[/C][C]240.8[/C][C]235.421300173079[/C][C]2.02254994462572[/C][C]5.37869982692081[/C][C]1.11907933058613[/C][/ROW]
[ROW][C]7[/C][C]241.1[/C][C]239.702766329693[/C][C]2.03259397175791[/C][C]1.39723367030696[/C][C]0.079728309552817[/C][/ROW]
[ROW][C]8[/C][C]223.8[/C][C]223.266284928829[/C][C]1.95090573234647[/C][C]0.533715071170602[/C][C]-0.651859538846223[/C][/ROW]
[ROW][C]9[/C][C]206.1[/C][C]205.241090541669[/C][C]1.86295109053341[/C][C]0.85890945833095[/C][C]-0.705043251189397[/C][/ROW]
[ROW][C]10[/C][C]174.7[/C][C]174.848619215758[/C][C]1.72155192048466[/C][C]-0.148619215757786[/C][C]-1.13842391295259[/C][/ROW]
[ROW][C]11[/C][C]203.3[/C][C]199.012281201532[/C][C]1.81950310429985[/C][C]4.28771879846847[/C][C]0.792065735284206[/C][/ROW]
[ROW][C]12[/C][C]220.5[/C][C]217.915680313966[/C][C]1.89374371964649[/C][C]2.58431968603427[/C][C]0.602949240581813[/C][/ROW]
[ROW][C]13[/C][C]299.5[/C][C]287.119967185514[/C][C]-0.866286518945028[/C][C]12.3800328144861[/C][C]2.81835605759697[/C][/ROW]
[ROW][C]14[/C][C]347.4[/C][C]342.091035583522[/C][C]0.345041633533459[/C][C]5.3089644164784[/C][C]1.71406623303384[/C][/ROW]
[ROW][C]15[/C][C]338.3[/C][C]340.805584833706[/C][C]0.330679669543845[/C][C]-2.5055848337063[/C][C]-0.0570960657932064[/C][/ROW]
[ROW][C]16[/C][C]327.7[/C][C]327.384771365565[/C][C]0.296517192068856[/C][C]0.31522863443524[/C][C]-0.485075003887567[/C][/ROW]
[ROW][C]17[/C][C]351.6[/C][C]352.198659786589[/C][C]0.347969527091625[/C][C]-0.598659786588605[/C][C]0.864537394019871[/C][/ROW]
[ROW][C]18[/C][C]396.6[/C][C]389.707090870285[/C][C]0.435730446871784[/C][C]6.89290912971476[/C][C]1.31027909171082[/C][/ROW]
[ROW][C]19[/C][C]438.8[/C][C]434.195183208462[/C][C]0.542654594013773[/C][C]4.60481679153763[/C][C]1.55328232940504[/C][/ROW]
[ROW][C]20[/C][C]395.6[/C][C]401.566478784849[/C][C]0.462385326091128[/C][C]-5.96647878484908[/C][C]-1.16961955991549[/C][/ROW]
[ROW][C]21[/C][C]363.5[/C][C]364.85566584471[/C][C]0.372703749442631[/C][C]-1.35566584470961[/C][C]-1.31072264508491[/C][/ROW]
[ROW][C]22[/C][C]378.8[/C][C]378.129570961286[/C][C]0.404665138755364[/C][C]0.670429038713891[/C][C]0.454924288636024[/C][/ROW]
[ROW][C]23[/C][C]357[/C][C]356.927376061424[/C][C]0.347281253224218[/C][C]0.0726239385755184[/C][C]-0.762102209796267[/C][/ROW]
[ROW][C]24[/C][C]369[/C][C]370.277219656572[/C][C]0.351864718274208[/C][C]-1.27721965657196[/C][C]0.458058859970499[/C][/ROW]
[ROW][C]25[/C][C]464.8[/C][C]447.403222131374[/C][C]-0.927696206594951[/C][C]17.3967778686262[/C][C]2.90606483608869[/C][/ROW]
[ROW][C]26[/C][C]479.1[/C][C]472.858100619748[/C][C]-0.610536587133899[/C][C]6.24189938025158[/C][C]0.869432018576513[/C][/ROW]
[ROW][C]27[/C][C]431.3[/C][C]439.185769942804[/C][C]-0.875424103715443[/C][C]-7.88576994280393[/C][C]-1.1531225881721[/C][/ROW]
[ROW][C]28[/C][C]366.5[/C][C]377.229860064236[/C][C]-1.04053988260808[/C][C]-10.7298600642361[/C][C]-2.15414684255234[/C][/ROW]
[ROW][C]29[/C][C]326.3[/C][C]335.250583820214[/C][C]-1.10629603993556[/C][C]-8.95058382021411[/C][C]-1.44336253903777[/C][/ROW]
[ROW][C]30[/C][C]355.1[/C][C]350.895038431418[/C][C]-1.07691677889932[/C][C]4.20496156858156[/C][C]0.590497729354717[/C][/ROW]
[ROW][C]31[/C][C]331.6[/C][C]329.077140524318[/C][C]-1.11600083883243[/C][C]2.52285947568242[/C][C]-0.731138065390755[/C][/ROW]
[ROW][C]32[/C][C]261.3[/C][C]272.30519024967[/C][C]-1.22218744284703[/C][C]-11.0051902496704[/C][C]-1.96191985127129[/C][/ROW]
[ROW][C]33[/C][C]249[/C][C]253.820401517309[/C][C]-1.25560323926166[/C][C]-4.82040151730855[/C][C]-0.608539854017583[/C][/ROW]
[ROW][C]34[/C][C]205.5[/C][C]208.749542376002[/C][C]-1.34652182175515[/C][C]-3.24954237600225[/C][C]-1.54483896728508[/C][/ROW]
[ROW][C]35[/C][C]235.6[/C][C]231.55129197533[/C][C]-1.29917918529354[/C][C]4.04870802467004[/C][C]0.851482368122667[/C][/ROW]
[ROW][C]36[/C][C]240.9[/C][C]248.602669675485[/C][C]-1.31909860543149[/C][C]-7.70266967548536[/C][C]0.646708041555449[/C][/ROW]
[ROW][C]37[/C][C]264.9[/C][C]249.752837484753[/C][C]-1.34133747573983[/C][C]15.1471625152468[/C][C]0.0902649582684895[/C][/ROW]
[ROW][C]38[/C][C]253.8[/C][C]244.154386286834[/C][C]-1.37351268958206[/C][C]9.64561371316629[/C][C]-0.144411626151731[/C][/ROW]
[ROW][C]39[/C][C]232.3[/C][C]234.456388296084[/C][C]-1.43259688180749[/C][C]-2.1563882960838[/C][C]-0.289866009222027[/C][/ROW]
[ROW][C]40[/C][C]193.8[/C][C]204.250765094434[/C][C]-1.51987910587685[/C][C]-10.4507650944337[/C][C]-1.0143146242059[/C][/ROW]
[ROW][C]41[/C][C]177[/C][C]190.399575200062[/C][C]-1.53893995069403[/C][C]-13.3995752000624[/C][C]-0.434761230360682[/C][/ROW]
[ROW][C]42[/C][C]213.2[/C][C]204.64073378895[/C][C]-1.51565362123918[/C][C]8.55926621104964[/C][C]0.556244500728392[/C][/ROW]
[ROW][C]43[/C][C]207.2[/C][C]199.613085143013[/C][C]-1.52132418506037[/C][C]7.58691485698661[/C][C]-0.123788328013792[/C][/ROW]
[ROW][C]44[/C][C]180.6[/C][C]191.099878975541[/C][C]-1.53313243494333[/C][C]-10.4998789755408[/C][C]-0.246444132233647[/C][/ROW]
[ROW][C]45[/C][C]188.6[/C][C]187.563140344584[/C][C]-1.53668365457293[/C][C]1.03685965541644[/C][C]-0.0706273794046623[/C][/ROW]
[ROW][C]46[/C][C]175.4[/C][C]182.165907764184[/C][C]-1.54396395595142[/C][C]-6.7659077641838[/C][C]-0.136114307920771[/C][/ROW]
[ROW][C]47[/C][C]199[/C][C]195.084217657983[/C][C]-1.52386034931684[/C][C]3.91578234201741[/C][C]0.509765233392411[/C][/ROW]
[ROW][C]48[/C][C]179.6[/C][C]188.903317187931[/C][C]-1.51659122532706[/C][C]-9.30331718793088[/C][C]-0.164245010054623[/C][/ROW]
[ROW][C]49[/C][C]225.8[/C][C]205.989100997281[/C][C]-1.61610394738886[/C][C]19.8108990027191[/C][C]0.669002887638946[/C][/ROW]
[ROW][C]50[/C][C]234[/C][C]221.235381158511[/C][C]-1.528719126888[/C][C]12.7646188414887[/C][C]0.580156548370334[/C][/ROW]
[ROW][C]51[/C][C]200.2[/C][C]202.056803867243[/C][C]-1.63807052725028[/C][C]-1.85680386724347[/C][C]-0.61454564577158[/C][/ROW]
[ROW][C]52[/C][C]183.6[/C][C]193.312453744331[/C][C]-1.66109692683555[/C][C]-9.71245374433064[/C][C]-0.250356274749863[/C][/ROW]
[ROW][C]53[/C][C]178.2[/C][C]193.39329071464[/C][C]-1.65825526529454[/C][C]-15.1932907146402[/C][C]0.0614159072831315[/C][/ROW]
[ROW][C]54[/C][C]203.2[/C][C]193.955528846781[/C][C]-1.65526132934739[/C][C]9.24447115321894[/C][C]0.0782711117938521[/C][/ROW]
[ROW][C]55[/C][C]208.5[/C][C]199.03164260544[/C][C]-1.64550505875814[/C][C]9.46835739456046[/C][C]0.237253734092753[/C][/ROW]
[ROW][C]56[/C][C]191.8[/C][C]201.554791213202[/C][C]-1.63896689692395[/C][C]-9.75479121320204[/C][C]0.146928138622828[/C][/ROW]
[ROW][C]57[/C][C]172.8[/C][C]175.476764505281[/C][C]-1.68024206937595[/C][C]-2.6767645052809[/C][C]-0.861486482531513[/C][/ROW]
[ROW][C]58[/C][C]148[/C][C]158.458749768417[/C][C]-1.70642204085156[/C][C]-10.4587497684171[/C][C]-0.540752472236454[/C][/ROW]
[ROW][C]59[/C][C]159.4[/C][C]154.326471217919[/C][C]-1.70870260983227[/C][C]5.07352878208063[/C][C]-0.0854812670148723[/C][/ROW]
[ROW][C]60[/C][C]154.5[/C][C]164.013375775531[/C][C]-1.72691031848988[/C][C]-9.51337577553066[/C][C]0.402089568679853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159873&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159873&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1235.1235.1000
2280.7277.763374983192.319733517519982.936625016809760.909065055017082
3264.6264.0553748346012.155155579821760.544625165398704-0.564652462682712
4240.7240.1668591043582.054445476301620.533140895641532-0.919372979780313
5201.4201.8337876822581.88116254008675-0.433787682258163-1.42550634513946
6240.8235.4213001730792.022549944625725.378699826920811.11907933058613
7241.1239.7027663296932.032593971757911.397233670306960.079728309552817
8223.8223.2662849288291.950905732346470.533715071170602-0.651859538846223
9206.1205.2410905416691.862951090533410.85890945833095-0.705043251189397
10174.7174.8486192157581.72155192048466-0.148619215757786-1.13842391295259
11203.3199.0122812015321.819503104299854.287718798468470.792065735284206
12220.5217.9156803139661.893743719646492.584319686034270.602949240581813
13299.5287.119967185514-0.86628651894502812.38003281448612.81835605759697
14347.4342.0910355835220.3450416335334595.30896441647841.71406623303384
15338.3340.8055848337060.330679669543845-2.5055848337063-0.0570960657932064
16327.7327.3847713655650.2965171920688560.31522863443524-0.485075003887567
17351.6352.1986597865890.347969527091625-0.5986597865886050.864537394019871
18396.6389.7070908702850.4357304468717846.892909129714761.31027909171082
19438.8434.1951832084620.5426545940137734.604816791537631.55328232940504
20395.6401.5664787848490.462385326091128-5.96647878484908-1.16961955991549
21363.5364.855665844710.372703749442631-1.35566584470961-1.31072264508491
22378.8378.1295709612860.4046651387553640.6704290387138910.454924288636024
23357356.9273760614240.3472812532242180.0726239385755184-0.762102209796267
24369370.2772196565720.351864718274208-1.277219656571960.458058859970499
25464.8447.403222131374-0.92769620659495117.39677786862622.90606483608869
26479.1472.858100619748-0.6105365871338996.241899380251580.869432018576513
27431.3439.185769942804-0.875424103715443-7.88576994280393-1.1531225881721
28366.5377.229860064236-1.04053988260808-10.7298600642361-2.15414684255234
29326.3335.250583820214-1.10629603993556-8.95058382021411-1.44336253903777
30355.1350.895038431418-1.076916778899324.204961568581560.590497729354717
31331.6329.077140524318-1.116000838832432.52285947568242-0.731138065390755
32261.3272.30519024967-1.22218744284703-11.0051902496704-1.96191985127129
33249253.820401517309-1.25560323926166-4.82040151730855-0.608539854017583
34205.5208.749542376002-1.34652182175515-3.24954237600225-1.54483896728508
35235.6231.55129197533-1.299179185293544.048708024670040.851482368122667
36240.9248.602669675485-1.31909860543149-7.702669675485360.646708041555449
37264.9249.752837484753-1.3413374757398315.14716251524680.0902649582684895
38253.8244.154386286834-1.373512689582069.64561371316629-0.144411626151731
39232.3234.456388296084-1.43259688180749-2.1563882960838-0.289866009222027
40193.8204.250765094434-1.51987910587685-10.4507650944337-1.0143146242059
41177190.399575200062-1.53893995069403-13.3995752000624-0.434761230360682
42213.2204.64073378895-1.515653621239188.559266211049640.556244500728392
43207.2199.613085143013-1.521324185060377.58691485698661-0.123788328013792
44180.6191.099878975541-1.53313243494333-10.4998789755408-0.246444132233647
45188.6187.563140344584-1.536683654572931.03685965541644-0.0706273794046623
46175.4182.165907764184-1.54396395595142-6.7659077641838-0.136114307920771
47199195.084217657983-1.523860349316843.915782342017410.509765233392411
48179.6188.903317187931-1.51659122532706-9.30331718793088-0.164245010054623
49225.8205.989100997281-1.6161039473888619.81089900271910.669002887638946
50234221.235381158511-1.52871912688812.76461884148870.580156548370334
51200.2202.056803867243-1.63807052725028-1.85680386724347-0.61454564577158
52183.6193.312453744331-1.66109692683555-9.71245374433064-0.250356274749863
53178.2193.39329071464-1.65825526529454-15.19329071464020.0614159072831315
54203.2193.955528846781-1.655261329347399.244471153218940.0782711117938521
55208.5199.03164260544-1.645505058758149.468357394560460.237253734092753
56191.8201.554791213202-1.63896689692395-9.754791213202040.146928138622828
57172.8175.476764505281-1.68024206937595-2.6767645052809-0.861486482531513
58148158.458749768417-1.70642204085156-10.4587497684171-0.540752472236454
59159.4154.326471217919-1.708702609832275.07352878208063-0.0854812670148723
60154.5164.013375775531-1.72691031848988-9.513375775530660.402089568679853



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')